pca: pca

In multifluo: Dealing with Several Images of a Same Object Constituted of Different Zones

Description

Calculates Principal Component Analysis with agreement ellipses

Usage

pca(data, zone = "zone", pixel = NULL)

Arguments

data	should contain a column named as zone, and another named pixel
zone	name of the column containing the zone
pixel	name of the column containing the pixel name (required when ellipses, individual projections or tests are asked in the PCA)

Value

A list containing

B	matrix of zone covariance
IndSup	supplementary individuals
EingenVectors	eigen vectors obtained by the PCA
EingenValues	eigen values obtained by the PCA
IndivCoord	coordinates of the individuals (here, zone means)
VarCoord	coordinates of the variables
NbdimSig	number of significant dimensions

References

Peltier, C., Visalli, M. and Schlich, P. (2015), Canonical Variate Analysis of Sensory Profiling Data. J Sens Stud, 30: 316 328. doi:10.1111/joss.12160

See Also

plotpca reshapimg

Examples

data(df.scaled)
resPCA=pca(data=df.scaled[,-1], zone="zone",pixel="pixel")

multifluo documentation built on May 2, 2019, 1:07 p.m.